{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(t):\n",
    "    return 1 / (1 + np.exp(-t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 500)\n",
    "y = sigmoid(x)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
